18 research outputs found

    Mitochondrial oxodicarboxylate carrier deficiency is associated with mitochondrial DNA depletion and spinal muscular atrophy-like disease.

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    PURPOSE: To understand the role of the mitochondrial oxodicarboxylate carrier (SLC25A21) in the development of spinal muscular atrophy-like disease. METHODS: We identified a novel pathogenic variant in a patient by whole-exome sequencing. The pathogenicity of the mutation was studied by transport assays, computer modeling, followed by targeted metabolic testing and in vitro studies in human fibroblasts and neurons. RESULTS: The patient carries a homozygous pathogenic variant c.695A>G; p.(Lys232Arg) in the SLC25A21 gene, encoding the mitochondrial oxodicarboxylate carrier, and developed spinal muscular atrophy and mitochondrial myopathy. Transport assays show that the mutation renders SLC25A21 dysfunctional and 2-oxoadipate cannot be imported into the mitochondrial matrix. Computer models of central metabolism predicted that impaired transport of oxodicarboxylate disrupts the pathways of lysine and tryptophan degradation, and causes accumulation of 2-oxoadipate, pipecolic acid, and quinolinic acid, which was confirmed in the patient's urine by targeted metabolomics. Exposure to 2-oxoadipate and quinolinic acid decreased the level of mitochondrial complexes in neuronal cells (SH-SY5Y) and induced apoptosis. CONCLUSION: Mitochondrial oxodicarboxylate carrier deficiency leads to mitochondrial dysfunction and the accumulation of oxoadipate and quinolinic acid, which in turn cause toxicity in spinal motor neurons leading to spinal muscular atrophy-like disease

    Sources of variation for indoor nitrogen dioxide in rural residences of Ethiopia

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    <p>Abstract</p> <p>Background</p> <p>Unprocessed biomass fuel is the primary source of indoor air pollution (IAP) in developing countries. The use of biomass fuel has been linked with acute respiratory infections. This study assesses sources of variations associated with the level of indoor nitrogen dioxide (NO<sub>2</sub>).</p> <p>Materials and methods</p> <p>This study examines household factors affecting the level of indoor pollution by measuring NO<sub>2</sub>. Repeated measurements of NO<sub>2 </sub>were made using a passive diffusive sampler. A <it>Saltzman </it>colorimetric method using a spectrometer calibrated at 540 nm was employed to analyze the mass of NO<sub>2 </sub>on the collection filter that was then subjected to a mass transfer equation to calculate the level of NO<sub>2 </sub>for the 24 hours of sampling duration. Structured questionnaire was used to collect data on fuel use characteristics. Data entry and cleaning was done in EPI INFO version 6.04, while data was analyzed using SPSS version 15.0. Analysis of variance, multiple linear regression and linear mixed model were used to isolate determining factors contributing to the variation of NO<sub>2 </sub>concentration.</p> <p>Results</p> <p>A total of 17,215 air samples were fully analyzed during the study period. Wood and crop were principal source of household energy. Biomass fuel characteristics were strongly related to indoor NO<sub>2 </sub>concentration in one-way analysis of variance. There was variation in repeated measurements of indoor NO<sub>2 </sub>over time. In a linear mixed model regression analysis, highland setting, wet season, cooking, use of fire events at least twice a day, frequency of cooked food items, and interaction between ecology and season were predictors of indoor NO<sub>2 </sub>concentration. The volume of the housing unit and the presence of kitchen showed little relevance in the level of NO<sub>2 </sub>concentration.</p> <p>Conclusion</p> <p>Agro-ecology, season, purpose of fire events, frequency of fire activities, frequency of cooking and physical conditions of housing are predictors of NO<sub>2 </sub>concentration. Improved kitchen conditions and ventilation are highly recommended.</p

    Ischaemic accumulation of succinate controls reperfusion injury through mitochondrial ROS.

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    Ischaemia-reperfusion injury occurs when the blood supply to an organ is disrupted and then restored, and underlies many disorders, notably heart attack and stroke. While reperfusion of ischaemic tissue is essential for survival, it also initiates oxidative damage, cell death and aberrant immune responses through the generation of mitochondrial reactive oxygen species (ROS). Although mitochondrial ROS production in ischaemia reperfusion is established, it has generally been considered a nonspecific response to reperfusion. Here we develop a comparative in vivo metabolomic analysis, and unexpectedly identify widely conserved metabolic pathways responsible for mitochondrial ROS production during ischaemia reperfusion. We show that selective accumulation of the citric acid cycle intermediate succinate is a universal metabolic signature of ischaemia in a range of tissues and is responsible for mitochondrial ROS production during reperfusion. Ischaemic succinate accumulation arises from reversal of succinate dehydrogenase, which in turn is driven by fumarate overflow from purine nucleotide breakdown and partial reversal of the malate/aspartate shuttle. After reperfusion, the accumulated succinate is rapidly re-oxidized by succinate dehydrogenase, driving extensive ROS generation by reverse electron transport at mitochondrial complex I. Decreasing ischaemic succinate accumulation by pharmacological inhibition is sufficient to ameliorate in vivo ischaemia-reperfusion injury in murine models of heart attack and stroke. Thus, we have identified a conserved metabolic response of tissues to ischaemia and reperfusion that unifies many hitherto unconnected aspects of ischaemia-reperfusion injury. Furthermore, these findings reveal a new pathway for metabolic control of ROS production in vivo, while demonstrating that inhibition of ischaemic succinate accumulation and its oxidation after subsequent reperfusion is a potential therapeutic target to decrease ischaemia-reperfusion injury in a range of pathologies

    The poly-omics of ageing through individual-based metabolic modelling

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    Abstract Background Ageing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype. Results We propose a method based on poly-omic constraint-based models and machine learning in order to further the understanding of transcriptomic ageing. We use normalised CD4 T-cell gene expression data from peripheral blood mononuclear cells in 499 healthy individuals to create individual metabolic models. These models are then combined with a transcriptomic age predictor and chronological age to provide new insights into the differences between transcriptomic and chronological ageing. As a result, we propose a novel metabolic age predictor. Conclusions We show that our poly-omic predictors provide a more detailed analysis of transcriptomic ageing compared to gene-based approaches, and represent a basis for furthering our knowledge of the ageing mechanisms in human cells

    Adverse birth outcome: a comparative analysis between cesarean section and vaginal delivery at Felegehiwot Referral Hospital, Northwest Ethiopia: a retrospective record review [Corrigendum]

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    Abebe Eyowas F, Negasi AK, Aynalem GE, Worku AG. Pediatric Health, Medicine and Therapeutics. 2016;7:65&ndash;70On page 65 Abel Fekadu Dadi should have been listed as an author. The incorrect author list was:Fantu Abebe Eyowas1Ashebir Kidane Negasi1Gizachew Eyassu Aynalem1Abebaw Gebeyehu Worku2The correct author list should have been:Fantu Abebe Eyowas1Ashebir Kidane Negasi1Gizachew Eyassu Aynalem1Abebaw Gebeyehu Worku2Abel Fekadu Dadi2Read the original articl
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